Welcome to R!

Purpose of this tutorial

R is a great resource that has extremely good documentation, including several free books that systematically teach users the basics and then some. This tutorial is not meant to replace any such resources but to provide an inital project to try out R and R Studio. As such, it will present a streamlined process with jumping off points. If you are interested in learning more R there will be a recommended booklist at the end.

RStudio Cloud

To complete this tutorial, you will need to create an account with RStudio Cloud so navigate to their homepage and click Get Started.

Once you’ve made your account, you will be navigated to Your Workspace. Click the blue New Project button to the right of Your Projects and wait for the Deploying Project bubbles to dissappear.

R Script

Under File highlight New File and select R Script.

Go ahead and save the file. This will be where we will be executing all our commands.

Set up the environment

Open Tools and click on Global Options. There will be four sections, R Sessions, Workspace, History and Other. Under Workspace, uncheck Restore .RData into workspace at startup and change Save workspace to .RData on exit to never. Hit Ok to save and exit.

By changing these options, we are making it a little harder to pick up where you left off in a project after you close it, but this will make your code reproducible, allowing you to get the same results every time you run your code.

Run some code

Speaking of running code, R is a language that runs only selected code. What does that mean? It means that if your cursor is on a specific line, everything on that line is selected, and will be run. If you hightlight some of your code, only what is highlighted is selected and will be run.

Try it out by typing 2 + 2 on the first line of your blank R script and 4 + 4 on the next one, Hit the Run button at the upper right corner of your R script and see what happens, (you can also use the keyboard shortcut ctrl-enter).

All of the functions we will be using are well documented. If you ever have questions, run them in the console, (lower left pane) with a ?in front, e.g. ?help()

R and Data

Load the Tidyverse

This tutorial makes extensive use of the Tidyverse.

If you have never used the Tidyverse before, you are about to meet one of the best packages in R. Or group of packages. The Tidyverse contains multiple packages designed to clean data, wrangle it and graph it. The packages are compatible with each other, so you can easily move through each stage of cleaning, plotting and presenting.

To install the Tidyverse, either click on the Packages tab in the lower right pane, then the Install button in the left corner. Type in tidyverse and click Install. It will take a moment to run and it will fill your console with red text. The installation will finish with The downloaded source packages are in ‘/tmp/RtmpPmVnsD/downloaded_packages’. Now we can load the package into R using library(). This code should go at the top of your R Script.

library(tidyverse)
## -- Attaching packages ------------------------------------------------------ tidyverse 1.2.1 --
## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0
## -- Conflicts --------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Let’s load one more package, maps, beginning with the installation.

library(maps)
## 
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
## 
##     map

We only need to install packages once, after that we can always load them for use using the library() call.

Loading data

We will be working with a semi-cleaned version of a UFO data set from Kaggle. The original data set can be found here. We will be only looking at data for the US. You can get the zipped file here: ufos.zip. R Studio Cloud only takes data in a zipped format, so we will upload the dataset into our environment as a zip file.

Under the Files tab in the lower right pane, click on Upload button. Find the zipped file called ufos on your computer and upload it. You should see ufos.csv appear in your files.

Let’s read in the csv as a tibble, which is a slightly more flexible version of a data frame and store it as a variable which we will use to call it for the rest of the lab.

ufos <- as_tibble(read_csv("ufos.csv"))

You will get a print out that looks similar to the one below.

You can ignore it.

You should see ufos show up as data set in your Global Enviroment on the upper right pane of your screen. If you click on it, you will open the data in a new tab.

Look at our data

Let’s take a quick look at our data.

glimpse(ufos)
## Observations: 65,114
## Variables: 14
## $ datetime             <dttm> 1949-10-10 20:30:00, 1956-10-10 21:00:00...
## $ city                 <chr> "san marcos", "edna", "kaneohe", "bristol...
## $ state                <chr> "tx", "tx", "hi", "tn", "ct", "al", "fl",...
## $ country              <chr> "us", "us", "us", "us", "us", "us", "us",...
## $ shape                <chr> "cylinder", "circle", "light", "sphere", ...
## $ duration..seconds.   <dbl> 2700, 20, 900, 300, 1200, 180, 120, 300, ...
## $ duration..hours.min. <chr> "45 minutes", "1/2 hour", "15 minutes", "...
## $ comments             <chr> "This event took place in early fall arou...
## $ date.posted          <date> 2004-04-27, 2004-01-17, 2004-01-22, 2007...
## $ latitude             <dbl> 29.88306, 28.97833, 21.41806, 36.59500, 4...
## $ longitude            <dbl> -97.94111, -96.64583, -157.80361, -82.188...
## $ year                 <dbl> 1949, 1956, 1960, 1961, 1965, 1966, 1966,...
## $ month                <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1...
## $ day                  <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1...

The glimpse() function gives us a compressed view of the data, but here’s a few things we can gather from the print out.

  • We have 14 variables and 65,144 observations
  • 7 variable names are related to time
  • 5 variable names are related to place
  • We have 2 variables in date format, 6 in character and 6 in numerical format.

Form some questions

Now that we’ve made some observations about our data, let’s ask some questions:

  • Which state has the most sightings?
  • Have the number of sightings increased over time?
  • Which shapes are seen most commonly?


Plotting

Plot 1: Which state has the most sightings?

Let’s start with the data. We want to know which state has seen the most ufos, which means we want the total number of observations for each state.

To do this, we’re going to use the count function. We give it a column name, (a variable), and it will count the rows, (observations), according to each value in the column.

This works well with a discrete variable but would not work well with a continuous variable. 

ufos %>% 
  count(state)
## # A tibble: 52 x 2
##    state     n
##    <chr> <int>
##  1 ak      319
##  2 al      642
##  3 ar      588
##  4 az     2414
##  5 ca     8912
##  6 co     1413
##  7 ct      892
##  8 dc        7
##  9 de      166
## 10 fl     3835
## # ... with 42 more rows

Throughout this tutorial we will be using what is called a pipe:%>%. When R runs a line of code with a pipe at the end, it takes that information and passes it on to the next line, which is also run. If we were to write the function above without pipes, it would look like this count(x = ufos, vars = state)

However, R is a positional language when it comes to functions, meaning we don’t have to write out what each entry is in the count function, if we give it the correct pieces in order, i.e. count(ufos, state).


Let’s arrange the data so we can see which states have the most observations. We will use a pipe so we don’t have to save our data in a variable. Since arrange() automatically sorts from least to greatest, we will use desc() to flip this.

ufos %>% 
  count(state) %>% 
  arrange(desc(n))
## # A tibble: 52 x 2
##    state     n
##    <chr> <int>
##  1 ca     8912
##  2 wa     3966
##  3 fl     3835
##  4 tx     3447
##  5 ny     2980
##  6 il     2499
##  7 az     2414
##  8 pa     2366
##  9 oh     2275
## 10 mi     1836
## # ... with 42 more rows

If we want to see all the data, we can always pipe the data into a view(), but let’s go even farther. Let’s make a graph. We are going to use ggplot2, the part of the Tidyverse designed for graphing. There are three things you need to know immediately to use ggplot.

  1. To create a graph, you will always need to call ggplot() and then the type of chart you want, a geom. ggplot() creates the base layer that you will add plots to, the geoms are the charts you will be adding.
  2. There are two parts to each ggplot() or geom function, the aes() or aesthetics and the function itself. The data set, and anything you don’t want to vary will be written the function, while any variables you want to vary per observation will be in the aes().
  3. ggplot takes information in order, which means that if you write your data in the right order you won’t have to write out the full equality (like we do below).
  4. We use + as instead of %>% with ggplot2.
ufos %>%
  count(state) %>% 
  ggplot(mapping = aes(x = state, y = n)) +
  geom_bar(stat = "identity")

  • We can pipe our data right into the plot.
  • geom_bar() requires stat = 'identity' to use n as the height of each bar. Use ?geom_bar() to find out why.

It is pretty hard to read this chart, so let’s filter our data to just look at which states had more than 1000 observations. We can just add the filter() between the count() and the plot. Let’s also tack a coord_flip() on which renders x values along the y axis and y values along the x axis.

ufos %>% 
  count(state) %>% 
  filter(n >= 1000) %>% 
  ggplot(aes(state, n)) +
  geom_bar(stat = "identity") +
  coord_flip()

Plot 2: How have sightings changed over time?

California is by far winning for the amount of sightings, so let’s see if there is a change over time.

First, let’s filter by state, and then count up the number of observations by year. We can pipe that data directly into a bar chart.

ufos %>% 
  filter(state == "ca") %>% 
  count(year) %>% 
  ggplot(aes(year, n)) +
  geom_bar(stat = "identity")

Try making some of these other plots:

Plot 3: Which shapes are seen most commonly?

Take a moment to think about how to construct this before looking at the code.

ufos %>% 
  count(shape) %>% 
  filter(n >= 1000) %>% 
  ggplot(aes(shape, n)) +
  geom_bar(stat = "identity") +
  coord_flip()

Note:

  • We added coord_flip() so that the terms would be readable.
  • Having trouble understanding how to use the functions? use ?filter(), ?count() or ?geom_bar() to see what’s tripping you up.

Appendix

Congratulations on finishing the tutorial! Now see if you can think up and answer some of your own questions!

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